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WIREs Data Mining Knowl Discov
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Temporal interval pattern languages to characterize time flow

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Knowledge discovery from temporal data (e.g., time series) is among the most challenging problems in data mining. Compared to static representations like rules or decision trees, the temporal component greatly increases the pattern diversity. It is important to keep the human perception of time flow in mind when representing temporal patterns, otherwise we open the floodgates to misinterpretation and misconception. This article gives an overview of temporal interval patterns, which are considered as being a well‐suited mechanism of knowledge representation, and focusses on the various pattern representation languages. Four typical phenomena in temporal data, and how the pattern languages can cope with them, are discussed. Given the domain knowledge, this provides the reader some guidance on which pattern language may be best‐suited for a given application. WIREs Data Mining Knowl Discov 2014, 4:196–212. doi: 10.1002/widm.1122 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation
Pictorial example of starting‐up a car: time on horizontal axis and vertical axis displays the observed attributes.
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Building blocks of most approaches to temporal pattern mining.
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Three state sequences (a)–(c) where slight variations in the end‐points lead to different interval relationships.
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Group‐wise time warping distorts selected interval relationships.
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Representing the time constraint of Figure by a pattern graph.
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‘A peak in x while y is increasing’, where the peak is expressed by a pair of ‘x increasing’ and ‘’x decreasing' implicitly requires that ‘y increasing’ holds without interruption—otherwise the (invalid) example on the right matches the pattern, too.
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Three problematic cases for A1 (first row), TSKR (second row), and semi‐interval sequential patterns (SISP; third row).
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An example pattern (P) expressed by (1) containment pattern, (2) A1/A2/Fluent, (3) relationship matrix (RM), (4) TSKR, (5) SIPO, (6) coincidence representation (CR), and finally (7) pattern graphs (PG), (8) TCSP/STP.
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All 13 possible qualitative relationships of interval A (gray) with respect to interval B (white) as defined by Allen.
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Poor thresholds and inappropriate smoothing (left) leading to fragmented intervals.
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Fundamental Concepts of Data and Knowledge > Knowledge Representation
Algorithmic Development > Spatial and Temporal Data Mining

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